Overview

Dataset statistics

Number of variables23
Number of observations2113
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory379.8 KiB
Average record size in memory184.1 B

Variable types

Numeric20
Categorical3

Warnings

baseline value is highly correlated with histogram_mode and 2 other fieldsHigh correlation
light_decelerations is highly correlated with mean_value_of_short_term_variability and 4 other fieldsHigh correlation
prolongued_decelerations is highly correlated with histogram_varianceHigh correlation
mean_value_of_short_term_variability is highly correlated with light_decelerations and 4 other fieldsHigh correlation
histogram_width is highly correlated with light_decelerations and 5 other fieldsHigh correlation
histogram_min is highly correlated with light_decelerations and 4 other fieldsHigh correlation
histogram_max is highly correlated with histogram_width and 1 other fieldsHigh correlation
histogram_number_of_peaks is highly correlated with mean_value_of_short_term_variability and 3 other fieldsHigh correlation
histogram_mode is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_mean is highly correlated with baseline value and 3 other fieldsHigh correlation
histogram_median is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_variance is highly correlated with light_decelerations and 4 other fieldsHigh correlation
baseline value is highly correlated with histogram_mode and 2 other fieldsHigh correlation
light_decelerations is highly correlated with mean_value_of_short_term_variability and 3 other fieldsHigh correlation
abnormal_short_term_variability is highly correlated with mean_value_of_short_term_variabilityHigh correlation
mean_value_of_short_term_variability is highly correlated with light_decelerations and 6 other fieldsHigh correlation
percentage_of_time_with_abnormal_long_term_variability is highly correlated with mean_value_of_short_term_variability and 2 other fieldsHigh correlation
histogram_width is highly correlated with light_decelerations and 6 other fieldsHigh correlation
histogram_min is highly correlated with light_decelerations and 4 other fieldsHigh correlation
histogram_max is highly correlated with histogram_width and 2 other fieldsHigh correlation
histogram_number_of_peaks is highly correlated with mean_value_of_short_term_variability and 4 other fieldsHigh correlation
histogram_mode is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_mean is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_median is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_variance is highly correlated with light_decelerations and 6 other fieldsHigh correlation
baseline value is highly correlated with histogram_mode and 2 other fieldsHigh correlation
light_decelerations is highly correlated with histogram_varianceHigh correlation
mean_value_of_short_term_variability is highly correlated with percentage_of_time_with_abnormal_long_term_variability and 3 other fieldsHigh correlation
percentage_of_time_with_abnormal_long_term_variability is highly correlated with mean_value_of_short_term_variability and 1 other fieldsHigh correlation
histogram_width is highly correlated with mean_value_of_short_term_variability and 3 other fieldsHigh correlation
histogram_min is highly correlated with mean_value_of_short_term_variability and 3 other fieldsHigh correlation
histogram_number_of_peaks is highly correlated with histogram_width and 1 other fieldsHigh correlation
histogram_mode is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_mean is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_median is highly correlated with baseline value and 2 other fieldsHigh correlation
histogram_variance is highly correlated with light_decelerations and 4 other fieldsHigh correlation
baseline value is highly correlated with histogram_median and 6 other fieldsHigh correlation
histogram_median is highly correlated with baseline value and 9 other fieldsHigh correlation
histogram_number_of_peaks is highly correlated with histogram_min and 2 other fieldsHigh correlation
fetal_health is highly correlated with histogram_median and 6 other fieldsHigh correlation
histogram_tendency is highly correlated with histogram_median and 1 other fieldsHigh correlation
severe_decelerations is highly correlated with histogram_median and 2 other fieldsHigh correlation
mean_value_of_long_term_variability is highly correlated with mean_value_of_short_term_variabilityHigh correlation
light_decelerations is highly correlated with histogram_min and 4 other fieldsHigh correlation
histogram_min is highly correlated with baseline value and 10 other fieldsHigh correlation
df_index is highly correlated with baseline value and 8 other fieldsHigh correlation
mean_value_of_short_term_variability is highly correlated with histogram_number_of_peaks and 8 other fieldsHigh correlation
percentage_of_time_with_abnormal_long_term_variability is highly correlated with fetal_health and 3 other fieldsHigh correlation
histogram_mean is highly correlated with baseline value and 11 other fieldsHigh correlation
abnormal_short_term_variability is highly correlated with baseline value and 8 other fieldsHigh correlation
histogram_mode is highly correlated with baseline value and 10 other fieldsHigh correlation
histogram_width is highly correlated with baseline value and 9 other fieldsHigh correlation
uterine_contractions is highly correlated with df_indexHigh correlation
histogram_variance is highly correlated with light_decelerations and 6 other fieldsHigh correlation
prolongued_decelerations is highly correlated with histogram_median and 4 other fieldsHigh correlation
histogram_max is highly correlated with histogram_mode and 1 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
accelerations has 886 (41.9%) zeros Zeros
fetal_movement has 1302 (61.6%) zeros Zeros
uterine_contractions has 323 (15.3%) zeros Zeros
light_decelerations has 1218 (57.6%) zeros Zeros
prolongued_decelerations has 1935 (91.6%) zeros Zeros
percentage_of_time_with_abnormal_long_term_variability has 1235 (58.4%) zeros Zeros
mean_value_of_long_term_variability has 137 (6.5%) zeros Zeros
histogram_number_of_peaks has 107 (5.1%) zeros Zeros
histogram_number_of_zeroes has 1611 (76.2%) zeros Zeros
histogram_variance has 186 (8.8%) zeros Zeros

Reproduction

Analysis started2021-07-28 15:47:19.411317
Analysis finished2021-07-28 15:48:44.276477
Duration1 minute and 24.87 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2113
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1064.640322
Minimum0
Maximum2125
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:44.396981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106.6
Q1533
median1065
Q31597
95-th percentile2019.4
Maximum2125
Range2125
Interquartile range (IQR)1064

Descriptive statistics

Standard deviation614.3049652
Coefficient of variation (CV)0.5770070442
Kurtosis-1.202061953
Mean1064.640322
Median Absolute Deviation (MAD)532
Skewness-0.004772609455
Sum2249585
Variance377370.5903
MonotonicityStrictly increasing
2021-07-28T17:48:44.551909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
6211
 
< 0.1%
6491
 
< 0.1%
6471
 
< 0.1%
6451
 
< 0.1%
6431
 
< 0.1%
6411
 
< 0.1%
6391
 
< 0.1%
6371
 
< 0.1%
6351
 
< 0.1%
Other values (2103)2103
99.5%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
21251
< 0.1%
21241
< 0.1%
21231
< 0.1%
21221
< 0.1%
21211
< 0.1%
21201
< 0.1%
21191
< 0.1%
21181
< 0.1%
21171
< 0.1%
21161
< 0.1%

baseline value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.3047799
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:44.710818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.837450707
Coefficient of variation (CV)0.0737966839
Kurtosis-0.2820935819
Mean133.3047799
Median Absolute Deviation (MAD)7
Skewness0.01975960793
Sum281673
Variance96.77543642
MonotonicityNot monotonic
2021-07-28T17:48:44.903718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133136
 
6.4%
130111
 
5.3%
122106
 
5.0%
138102
 
4.8%
12591
 
4.3%
12885
 
4.0%
12078
 
3.7%
14277
 
3.6%
14476
 
3.6%
13276
 
3.6%
Other values (38)1175
55.6%
ValueCountFrequency (%)
1067
 
0.3%
11021
 
1.0%
11216
 
0.8%
11411
 
0.5%
11528
 
1.3%
1165
 
0.2%
1172
 
0.1%
1189
 
0.4%
11917
 
0.8%
12078
3.7%
ValueCountFrequency (%)
1601
 
< 0.1%
15912
0.6%
15810
 
0.5%
1574
 
0.2%
1564
 
0.2%
1548
 
0.4%
15217
0.8%
15114
0.7%
15026
1.2%
14918
0.9%

accelerations
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003188357785
Minimum0
Maximum0.019
Zeros886
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:45.068791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002
Q30.006
95-th percentile0.011
Maximum0.019
Range0.019
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.00387133463
Coefficient of variation (CV)1.2142096
Kurtosis0.7536530251
Mean0.003188357785
Median Absolute Deviation (MAD)0.002
Skewness1.200207435
Sum6.737
Variance1.498723182 × 10-5
MonotonicityNot monotonic
2021-07-28T17:48:45.190763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0886
41.9%
0.002159
 
7.5%
0.003159
 
7.5%
0.001143
 
6.8%
0.004117
 
5.5%
0.006112
 
5.3%
0.005109
 
5.2%
0.008103
 
4.9%
0.00790
 
4.3%
0.00960
 
2.8%
Other values (10)175
 
8.3%
ValueCountFrequency (%)
0886
41.9%
0.001143
 
6.8%
0.002159
 
7.5%
0.003159
 
7.5%
0.004117
 
5.5%
0.005109
 
5.2%
0.006112
 
5.3%
0.00790
 
4.3%
0.008103
 
4.9%
0.00960
 
2.8%
ValueCountFrequency (%)
0.0191
 
< 0.1%
0.0182
 
0.1%
0.0174
 
0.2%
0.0167
 
0.3%
0.0159
 
0.4%
0.01420
 
0.9%
0.01322
1.0%
0.01224
1.1%
0.01136
1.7%
0.0150
2.4%

fetal_movement
Real number (ℝ≥0)

ZEROS

Distinct102
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009517274018
Minimum0
Maximum0.481
Zeros1302
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:45.351633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.0284
Maximum0.481
Range0.481
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.04680375707
Coefficient of variation (CV)4.917769204
Kurtosis63.85742334
Mean0.009517274018
Median Absolute Deviation (MAD)0
Skewness7.788014178
Sum20.11
Variance0.002190591676
MonotonicityNot monotonic
2021-07-28T17:48:45.590497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01302
61.6%
0.001164
 
7.8%
0.002112
 
5.3%
0.00387
 
4.1%
0.00448
 
2.3%
0.00536
 
1.7%
0.00631
 
1.5%
0.00728
 
1.3%
0.00925
 
1.2%
0.0125
 
1.2%
Other values (92)255
 
12.1%
ValueCountFrequency (%)
01302
61.6%
0.001164
 
7.8%
0.002112
 
5.3%
0.00387
 
4.1%
0.00448
 
2.3%
0.00536
 
1.7%
0.00631
 
1.5%
0.00728
 
1.3%
0.00825
 
1.2%
0.00925
 
1.2%
ValueCountFrequency (%)
0.4811
< 0.1%
0.4771
< 0.1%
0.471
< 0.1%
0.4691
< 0.1%
0.4551
< 0.1%
0.4511
< 0.1%
0.4461
< 0.1%
0.4431
< 0.1%
0.4411
< 0.1%
0.431
< 0.1%

uterine_contractions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004387127307
Minimum0
Maximum0.015
Zeros323
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:45.757398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.005
Q30.007
95-th percentile0.009
Maximum0.015
Range0.015
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.002940647883
Coefficient of variation (CV)0.6702900731
Kurtosis-0.6284943424
Mean0.004387127307
Median Absolute Deviation (MAD)0.002
Skewness0.1542946258
Sum9.27
Variance8.647409973 × 10-6
MonotonicityNot monotonic
2021-07-28T17:48:45.885324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0323
15.3%
0.005290
13.7%
0.004242
11.5%
0.006231
10.9%
0.007216
10.2%
0.003211
10.0%
0.008160
7.6%
0.002159
7.5%
0.001118
 
5.6%
0.00982
 
3.9%
Other values (6)81
 
3.8%
ValueCountFrequency (%)
0323
15.3%
0.001118
 
5.6%
0.002159
7.5%
0.003211
10.0%
0.004242
11.5%
0.005290
13.7%
0.006231
10.9%
0.007216
10.2%
0.008160
7.6%
0.00982
 
3.9%
ValueCountFrequency (%)
0.0151
 
< 0.1%
0.0142
 
0.1%
0.0132
 
0.1%
0.01211
 
0.5%
0.01116
 
0.8%
0.0149
 
2.3%
0.00982
 
3.9%
0.008160
7.6%
0.007216
10.2%
0.006231
10.9%

light_decelerations
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019010885
Minimum0
Maximum0.015
Zeros1218
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:46.062224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.008
Maximum0.015
Range0.015
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.002965579647
Coefficient of variation (CV)1.559937713
Kurtosis2.483414983
Mean0.0019010885
Median Absolute Deviation (MAD)0
Skewness1.709469521
Sum4.017
Variance8.79466264 × 10-6
MonotonicityNot monotonic
2021-07-28T17:48:46.231588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01218
57.6%
0.001163
 
7.7%
0.003118
 
5.6%
0.002115
 
5.4%
0.004114
 
5.4%
0.005107
 
5.1%
0.00674
 
3.5%
0.00855
 
2.6%
0.00754
 
2.6%
0.00937
 
1.8%
Other values (6)58
 
2.7%
ValueCountFrequency (%)
01218
57.6%
0.001163
 
7.7%
0.002115
 
5.4%
0.003118
 
5.6%
0.004114
 
5.4%
0.005107
 
5.1%
0.00674
 
3.5%
0.00754
 
2.6%
0.00855
 
2.6%
0.00937
 
1.8%
ValueCountFrequency (%)
0.0153
 
0.1%
0.0147
 
0.3%
0.0138
 
0.4%
0.01212
 
0.6%
0.01113
 
0.6%
0.0115
 
0.7%
0.00937
1.8%
0.00855
2.6%
0.00754
2.6%
0.00674
3.5%

severe_decelerations
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
0.0
2106 
0.001
 
7

Length

Max length5
Median length3
Mean length3.006625651
Min length3

Characters and Unicode

Total characters6353
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02106
99.7%
0.0017
 
0.3%

Length

2021-07-28T17:48:46.534417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-28T17:48:46.653374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02106
99.7%
0.0017
 
0.3%

Most occurring characters

ValueCountFrequency (%)
04233
66.6%
.2113
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4240
66.7%
Other Punctuation2113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04233
99.8%
17
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.2113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04233
66.6%
.2113
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04233
66.6%
.2113
33.3%
17
 
0.1%

prolongued_decelerations
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0001594888784
Minimum0
Maximum0.005
Zeros1935
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:46.775292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.002
Maximum0.005
Range0.005
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0005916288859
Coefficient of variation (CV)3.70953067
Kurtosis20.36468771
Mean0.0001594888784
Median Absolute Deviation (MAD)0
Skewness4.308787183
Sum0.337
Variance3.500247386 × 10-7
MonotonicityNot monotonic
2021-07-28T17:48:46.935186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
01935
91.6%
0.00272
 
3.4%
0.00170
 
3.3%
0.00324
 
1.1%
0.0049
 
0.4%
0.0053
 
0.1%
ValueCountFrequency (%)
01935
91.6%
0.00170
 
3.3%
0.00272
 
3.4%
0.00324
 
1.1%
0.0049
 
0.4%
0.0053
 
0.1%
ValueCountFrequency (%)
0.0053
 
0.1%
0.0049
 
0.4%
0.00324
 
1.1%
0.00272
 
3.4%
0.00170
 
3.3%
01935
91.6%

abnormal_short_term_variability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct75
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.99384761
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:47.127090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.17778196
Coefficient of variation (CV)0.3655325715
Kurtosis-1.051458051
Mean46.99384761
Median Absolute Deviation (MAD)14
Skewness-0.01186307275
Sum99298
Variance295.0761932
MonotonicityNot monotonic
2021-07-28T17:48:47.290982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6062
 
2.9%
5861
 
2.9%
6559
 
2.8%
6458
 
2.7%
6358
 
2.7%
6157
 
2.7%
5154
 
2.6%
6250
 
2.4%
2248
 
2.3%
2546
 
2.2%
Other values (65)1560
73.8%
ValueCountFrequency (%)
122
 
0.1%
137
 
0.3%
144
 
0.2%
154
 
0.2%
1612
 
0.6%
1713
 
0.6%
1810
 
0.5%
1916
0.8%
2027
1.3%
2133
1.6%
ValueCountFrequency (%)
871
 
< 0.1%
864
 
0.2%
846
 
0.3%
834
 
0.2%
822
 
0.1%
817
 
0.3%
807
 
0.3%
7915
0.7%
7819
0.9%
7715
0.7%

mean_value_of_short_term_variability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.335021297
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:47.472876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8843677184
Coefficient of variation (CV)0.6624371616
Kurtosis4.688735107
Mean1.335021297
Median Absolute Deviation (MAD)0.5
Skewness1.656144394
Sum2820.9
Variance0.7821062614
MonotonicityNot monotonic
2021-07-28T17:48:48.298402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8122
 
5.8%
1.3121
 
5.7%
0.5120
 
5.7%
0.4118
 
5.6%
0.7117
 
5.5%
0.6113
 
5.3%
0.9112
 
5.3%
1.2106
 
5.0%
1.5100
 
4.7%
199
 
4.7%
Other values (47)985
46.6%
ValueCountFrequency (%)
0.246
 
2.2%
0.384
4.0%
0.4118
5.6%
0.5120
5.7%
0.6113
5.3%
0.7117
5.5%
0.8122
5.8%
0.9112
5.3%
199
4.7%
1.197
4.6%
ValueCountFrequency (%)
71
< 0.1%
6.91
< 0.1%
6.32
0.1%
61
< 0.1%
5.91
< 0.1%
5.71
< 0.1%
5.42
0.1%
5.31
< 0.1%
5.21
< 0.1%
52
0.1%

percentage_of_time_with_abnormal_long_term_variability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.795078088
Minimum0
Maximum91
Zeros1235
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:48.505284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.33707265
Coefficient of variation (CV)1.872070083
Kurtosis4.321165181
Mean9.795078088
Median Absolute Deviation (MAD)0
Skewness2.206038192
Sum20697
Variance336.2482333
MonotonicityNot monotonic
2021-07-28T17:48:48.709164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01235
58.4%
152
 
2.5%
244
 
2.1%
543
 
2.0%
439
 
1.8%
336
 
1.7%
833
 
1.6%
631
 
1.5%
1229
 
1.4%
1023
 
1.1%
Other values (77)548
25.9%
ValueCountFrequency (%)
01235
58.4%
152
 
2.5%
244
 
2.1%
336
 
1.7%
439
 
1.8%
543
 
2.0%
631
 
1.5%
722
 
1.0%
833
 
1.6%
922
 
1.0%
ValueCountFrequency (%)
914
0.2%
902
 
0.1%
881
 
< 0.1%
861
 
< 0.1%
851
 
< 0.1%
846
0.3%
821
 
< 0.1%
812
 
0.1%
791
 
< 0.1%
783
0.1%

mean_value_of_long_term_variability
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct249
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.166635116
Minimum0
Maximum50.7
Zeros137
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:48.953047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.5
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.632911851
Coefficient of variation (CV)0.689746973
Kurtosis4.165552246
Mean8.166635116
Median Absolute Deviation (MAD)3.1
Skewness1.343133347
Sum17256.1
Variance31.72969592
MonotonicityNot monotonic
2021-07-28T17:48:49.125939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0137
 
6.5%
6.729
 
1.4%
7.129
 
1.4%
6.525
 
1.2%
5.225
 
1.2%
9.524
 
1.1%
8.523
 
1.1%
6.823
 
1.1%
7.223
 
1.1%
5.623
 
1.1%
Other values (239)1752
82.9%
ValueCountFrequency (%)
0137
6.5%
0.14
 
0.2%
0.24
 
0.2%
0.39
 
0.4%
0.46
 
0.3%
0.511
 
0.5%
0.63
 
0.1%
0.74
 
0.2%
0.81
 
< 0.1%
0.95
 
0.2%
ValueCountFrequency (%)
50.71
< 0.1%
41.81
< 0.1%
40.81
< 0.1%
36.91
< 0.1%
35.71
< 0.1%
34.71
< 0.1%
33.51
< 0.1%
29.61
< 0.1%
29.51
< 0.1%
29.31
< 0.1%

histogram_width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct154
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.53525793
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:49.298826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q137
median68
Q3100
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)63

Descriptive statistics

Standard deviation39.00770626
Coefficient of variation (CV)0.5530242237
Kurtosis-0.9072401844
Mean70.53525793
Median Absolute Deviation (MAD)32
Skewness0.3110190028
Sum149041
Variance1521.601147
MonotonicityNot monotonic
2021-07-28T17:48:49.455743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3939
 
1.8%
10235
 
1.7%
2730
 
1.4%
3129
 
1.4%
9828
 
1.3%
9627
 
1.3%
8327
 
1.3%
2227
 
1.3%
9027
 
1.3%
4226
 
1.2%
Other values (144)1818
86.0%
ValueCountFrequency (%)
32
 
0.1%
52
 
0.1%
61
 
< 0.1%
73
 
0.1%
810
0.5%
96
 
0.3%
109
0.4%
1110
0.5%
1220
0.9%
1313
0.6%
ValueCountFrequency (%)
1801
 
< 0.1%
1766
0.3%
1632
 
0.1%
1621
 
< 0.1%
1615
0.2%
1582
 
0.1%
1533
 
0.1%
15010
0.5%
14911
0.5%
1488
0.4%

histogram_min
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct109
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.56460009
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:49.631634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.56226922
Coefficient of variation (CV)0.3159557054
Kurtosis-1.289706275
Mean93.56460009
Median Absolute Deviation (MAD)27
Skewness0.1171852265
Sum197702
Variance873.9277614
MonotonicityNot monotonic
2021-07-28T17:48:49.820539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5076
 
3.6%
5250
 
2.4%
12048
 
2.3%
7147
 
2.2%
6045
 
2.1%
6843
 
2.0%
6741
 
1.9%
10337
 
1.8%
5136
 
1.7%
6235
 
1.7%
Other values (99)1655
78.3%
ValueCountFrequency (%)
5076
3.6%
5136
1.7%
5250
2.4%
5332
1.5%
5427
 
1.3%
5520
 
0.9%
5619
 
0.9%
5722
 
1.0%
5821
 
1.0%
5917
 
0.8%
ValueCountFrequency (%)
1591
 
< 0.1%
1581
 
< 0.1%
1561
 
< 0.1%
1552
 
0.1%
1543
 
0.1%
1538
0.4%
1524
0.2%
1514
0.2%
1503
 
0.1%
1492
 
0.1%

histogram_max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct86
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.099858
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:50.037416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.94517548
Coefficient of variation (CV)0.1093552163
Kurtosis0.6354312197
Mean164.099858
Median Absolute Deviation (MAD)11
Skewness0.5755344148
Sum346743
Variance322.0293229
MonotonicityNot monotonic
2021-07-28T17:48:50.250279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15771
 
3.4%
17165
 
3.1%
15862
 
2.9%
15658
 
2.7%
15958
 
2.7%
15253
 
2.5%
17852
 
2.5%
15452
 
2.5%
16548
 
2.3%
17248
 
2.3%
Other values (76)1546
73.2%
ValueCountFrequency (%)
1222
 
0.1%
1232
 
0.1%
1253
 
0.1%
1265
0.2%
1272
 
0.1%
1284
 
0.2%
12910
0.5%
1308
0.4%
1317
0.3%
1324
 
0.2%
ValueCountFrequency (%)
2386
 
0.3%
2303
 
0.1%
2285
 
0.2%
2131
 
< 0.1%
2115
 
0.2%
2104
 
0.2%
2051
 
< 0.1%
2043
 
0.1%
20031
1.5%
19920
0.9%

histogram_number_of_peaks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.077141505
Minimum0
Maximum18
Zeros107
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:50.410200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.951664426
Coefficient of variation (CV)0.7239543741
Kurtosis0.4996022711
Mean4.077141505
Median Absolute Deviation (MAD)2
Skewness0.8898506714
Sum8615
Variance8.712322886
MonotonicityNot monotonic
2021-07-28T17:48:50.548121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1351
16.6%
2329
15.6%
3267
12.6%
4257
12.2%
5210
9.9%
6158
7.5%
7143
6.8%
0107
 
5.1%
8106
 
5.0%
967
 
3.2%
Other values (8)118
 
5.6%
ValueCountFrequency (%)
0107
 
5.1%
1351
16.6%
2329
15.6%
3267
12.6%
4257
12.2%
5210
9.9%
6158
7.5%
7143
6.8%
8106
 
5.0%
967
 
3.2%
ValueCountFrequency (%)
181
 
< 0.1%
162
 
0.1%
151
 
< 0.1%
145
 
0.2%
1310
 
0.5%
1222
 
1.0%
1128
 
1.3%
1049
2.3%
967
3.2%
8106
5.0%

histogram_number_of_zeroes
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3256034075
Minimum0
Maximum10
Zeros1611
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:50.720023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7077710075
Coefficient of variation (CV)2.173721132
Kurtosis30.20305483
Mean0.3256034075
Median Absolute Deviation (MAD)0
Skewness3.908034653
Sum688
Variance0.5009397991
MonotonicityNot monotonic
2021-07-28T17:48:50.870935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
01611
76.2%
1366
 
17.3%
2108
 
5.1%
321
 
1.0%
52
 
0.1%
42
 
0.1%
101
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
01611
76.2%
1366
 
17.3%
2108
 
5.1%
321
 
1.0%
42
 
0.1%
52
 
0.1%
71
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
52
 
0.1%
42
 
0.1%
321
 
1.0%
2108
 
5.1%
1366
 
17.3%
01611
76.2%

histogram_mode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct88
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.4543303
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:51.013853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.40202551
Coefficient of variation (CV)0.1193270919
Kurtosis3.013332983
Mean137.4543303
Median Absolute Deviation (MAD)10
Skewness-0.9974745543
Sum290441
Variance269.0264408
MonotonicityNot monotonic
2021-07-28T17:48:51.178758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133140
 
6.6%
13689
 
4.2%
15088
 
4.2%
14286
 
4.1%
14879
 
3.7%
14478
 
3.7%
12973
 
3.5%
14370
 
3.3%
12567
 
3.2%
12666
 
3.1%
Other values (78)1277
60.4%
ValueCountFrequency (%)
606
0.3%
675
0.2%
691
 
< 0.1%
711
 
< 0.1%
756
0.3%
761
 
< 0.1%
771
 
< 0.1%
8611
0.5%
886
0.3%
893
 
0.1%
ValueCountFrequency (%)
1871
 
< 0.1%
1866
0.3%
1804
 
0.2%
1791
 
< 0.1%
1766
0.3%
1704
 
0.2%
1693
 
0.1%
1678
0.4%
16510
0.5%
1641
 
< 0.1%

histogram_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct103
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.5996214
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:51.353657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.61042168
Coefficient of variation (CV)0.1159767131
Kurtosis0.934831212
Mean134.5996214
Median Absolute Deviation (MAD)10
Skewness-0.6520677422
Sum284409
Variance243.685265
MonotonicityNot monotonic
2021-07-28T17:48:51.522561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14365
 
3.1%
14463
 
3.0%
13563
 
3.0%
14161
 
2.9%
14060
 
2.8%
13259
 
2.8%
13358
 
2.7%
14757
 
2.7%
14557
 
2.7%
13657
 
2.7%
Other values (93)1513
71.6%
ValueCountFrequency (%)
731
 
< 0.1%
751
 
< 0.1%
761
 
< 0.1%
781
 
< 0.1%
791
 
< 0.1%
802
0.1%
811
 
< 0.1%
822
0.1%
834
0.2%
843
0.1%
ValueCountFrequency (%)
1821
< 0.1%
1801
< 0.1%
1781
< 0.1%
1751
< 0.1%
1732
0.1%
1721
< 0.1%
1712
0.1%
1701
< 0.1%
1691
< 0.1%
1681
< 0.1%

histogram_median
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.0894463
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:51.694461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median139
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.47895702
Coefficient of variation (CV)0.1048520174
Kurtosis0.6724117285
Mean138.0894463
Median Absolute Deviation (MAD)10
Skewness-0.4799423424
Sum291783
Variance209.6401963
MonotonicityNot monotonic
2021-07-28T17:48:51.866351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13768
 
3.2%
14268
 
3.2%
14668
 
3.2%
14567
 
3.2%
14764
 
3.0%
14163
 
3.0%
13462
 
2.9%
15162
 
2.9%
14960
 
2.8%
14056
 
2.7%
Other values (85)1475
69.8%
ValueCountFrequency (%)
771
< 0.1%
781
< 0.1%
792
0.1%
821
< 0.1%
861
< 0.1%
871
< 0.1%
901
< 0.1%
911
< 0.1%
922
0.1%
931
< 0.1%
ValueCountFrequency (%)
1861
< 0.1%
1831
< 0.1%
1801
< 0.1%
1781
< 0.1%
1771
< 0.1%
1762
0.1%
1742
0.1%
1721
< 0.1%
1711
< 0.1%
1702
0.1%

histogram_variance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct133
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.90724089
Minimum0
Maximum269
Zeros186
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2021-07-28T17:48:52.077245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile76
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation29.03876572
Coefficient of variation (CV)1.53585422
Kurtosis15.0457291
Mean18.90724089
Median Absolute Deviation (MAD)6
Skewness3.210222163
Sum39951
Variance843.2499144
MonotonicityNot monotonic
2021-07-28T17:48:52.251141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1246
 
11.6%
0186
 
8.8%
2163
 
7.7%
3158
 
7.5%
4106
 
5.0%
583
 
3.9%
874
 
3.5%
665
 
3.1%
753
 
2.5%
949
 
2.3%
Other values (123)930
44.0%
ValueCountFrequency (%)
0186
8.8%
1246
11.6%
2163
7.7%
3158
7.5%
4106
5.0%
583
 
3.9%
665
 
3.1%
753
 
2.5%
874
 
3.5%
949
 
2.3%
ValueCountFrequency (%)
2691
< 0.1%
2541
< 0.1%
2501
< 0.1%
2431
< 0.1%
2411
< 0.1%
2151
< 0.1%
1951
< 0.1%
1901
< 0.1%
1821
< 0.1%
1771
< 0.1%

histogram_tendency
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.1 KiB
0.0
1110 
1.0
838 
-1.0
165 

Length

Max length4
Median length3
Mean length3.078088027
Min length3

Characters and Unicode

Total characters6504
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.01110
52.5%
1.0838
39.7%
-1.0165
 
7.8%

Length

2021-07-28T17:48:52.582950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-28T17:48:52.679894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01110
52.5%
1.01003
47.5%

Most occurring characters

ValueCountFrequency (%)
03223
49.6%
.2113
32.5%
11003
 
15.4%
-165
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4226
65.0%
Other Punctuation2113
32.5%
Dash Punctuation165
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03223
76.3%
11003
 
23.7%
Other Punctuation
ValueCountFrequency (%)
.2113
100.0%
Dash Punctuation
ValueCountFrequency (%)
-165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03223
49.6%
.2113
32.5%
11003
 
15.4%
-165
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03223
49.6%
.2113
32.5%
11003
 
15.4%
-165
 
2.5%

fetal_health
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size123.9 KiB
1.0
1646 
2.0
292 
3.0
175 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6339
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01646
77.9%
2.0292
 
13.8%
3.0175
 
8.3%

Length

2021-07-28T17:48:52.996711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-28T17:48:53.087659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.01646
77.9%
2.0292
 
13.8%
3.0175
 
8.3%

Most occurring characters

ValueCountFrequency (%)
.2113
33.3%
02113
33.3%
11646
26.0%
2292
 
4.6%
3175
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4226
66.7%
Other Punctuation2113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02113
50.0%
11646
38.9%
2292
 
6.9%
3175
 
4.1%
Other Punctuation
ValueCountFrequency (%)
.2113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.2113
33.3%
02113
33.3%
11646
26.0%
2292
 
4.6%
3175
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.2113
33.3%
02113
33.3%
11646
26.0%
2292
 
4.6%
3175
 
2.8%

Interactions

2021-07-28T17:47:26.128724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:26.307621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:26.452551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:26.659441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:26.827335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.001235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.154151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.301062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.460975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.630868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.779787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:27.927702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:28.092607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:28.256499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:28.452388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:28.625300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:28.895132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:29.241932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:29.433820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:29.661691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:29.848596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:30.066459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:30.291334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:30.540187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:30.768053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:31.013912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:31.239785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:31.450662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:31.669553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:31.900401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:32.164269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:32.384142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:32.600013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:32.801897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:33.011762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:33.192657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:33.367572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:33.548455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:33.953222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:34.150108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:34.344994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:34.534900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:34.743782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:34.968636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:35.168539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:35.386885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:35.600767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:35.817638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:36.030519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:36.264368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:36.464266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:36.666135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:36.877029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:37.065906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:37.281781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:37.489668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:37.703537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:37.913417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:38.117300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:38.323182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:38.523067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:38.691988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:38.885857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:39.093737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:39.268639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:39.494509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:39.685412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:39.879286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:40.087168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:40.279055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:40.462964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:40.658842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:40.862739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:41.036621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:41.221513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:41.399425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:41.578308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:41.754209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:41.927108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:42.106004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:42.277919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:42.458815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:42.644713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:42.846580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:43.066465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:43.260354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:43.461243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:43.655116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:43.861011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:44.331737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:44.543615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:44.738491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:44.940392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:45.126268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:45.326151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:45.530047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:45.727921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:45.922824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:46.115711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:46.304593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:46.509484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:46.691384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:46.879271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:47.097146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:47.289025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:47.492907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:47.696787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:47.887691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:48.095572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:48.297457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:48.485334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:48.676224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:48.875110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:49.070001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:49.260887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:49.462785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:49.652665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:49.863540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:50.059427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:50.255330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:50.444220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:50.624107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:50.801005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:50.993904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:51.172787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:51.355700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:51.546586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:51.723471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:51.919360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:52.105264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:52.281163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:52.461061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:52.643953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:52.824850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:53.004752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:53.185628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:53.357548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:53.536440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:53.727315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-28T17:47:53.924206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-07-28T17:48:42.860058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-28T17:48:53.212591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-28T17:48:53.743282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-28T17:48:54.220008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-28T17:48:54.773689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-28T17:48:55.183452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-28T17:48:43.174872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-28T17:48:44.014611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexbaseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
00120.00.0000.00.0000.0000.00.00073.00.543.02.464.062.0126.02.00.0120.0137.0121.073.01.02.0
11132.00.0060.00.0060.0030.00.00017.02.10.010.4130.068.0198.06.01.0141.0136.0140.012.00.01.0
22133.00.0030.00.0080.0030.00.00016.02.10.013.4130.068.0198.05.01.0141.0135.0138.013.00.01.0
33134.00.0030.00.0080.0030.00.00016.02.40.023.0117.053.0170.011.00.0137.0134.0137.013.01.01.0
44132.00.0070.00.0080.0000.00.00016.02.40.019.9117.053.0170.09.00.0137.0136.0138.011.01.01.0
55134.00.0010.00.0100.0090.00.00226.05.90.00.0150.050.0200.05.03.076.0107.0107.0170.00.03.0
66134.00.0010.00.0130.0080.00.00329.06.30.00.0150.050.0200.06.03.071.0107.0106.0215.00.03.0
77122.00.0000.00.0000.0000.00.00083.00.56.015.668.062.0130.00.00.0122.0122.0123.03.01.03.0
88122.00.0000.00.0020.0000.00.00084.00.55.013.668.062.0130.00.00.0122.0122.0123.03.01.03.0
99122.00.0000.00.0030.0000.00.00086.00.36.010.668.062.0130.01.00.0122.0122.0123.01.01.03.0

Last rows

df_indexbaseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
21032116140.00.0040.0000.0040.0000.00.080.00.236.02.218.0140.0158.01.00.0147.0148.0149.01.00.01.0
21042117140.00.0000.0000.0080.0000.00.079.00.320.08.526.0124.0150.01.00.0144.0143.0145.01.01.01.0
21052118140.00.0000.0000.0060.0010.00.079.00.526.07.021.0129.0150.01.00.0145.0142.0145.02.01.01.0
21062119140.00.0000.0000.0070.0010.00.079.00.627.06.426.0124.0150.01.00.0144.0141.0145.01.01.01.0
21072120140.00.0000.0000.0050.0010.00.077.00.717.06.031.0124.0155.02.00.0145.0143.0145.02.00.01.0
21082121140.00.0000.0000.0070.0000.00.079.00.225.07.240.0137.0177.04.00.0153.0150.0152.02.00.02.0
21092122140.00.0010.0000.0070.0000.00.078.00.422.07.166.0103.0169.06.00.0152.0148.0151.03.01.02.0
21102123140.00.0010.0000.0070.0000.00.079.00.420.06.167.0103.0170.05.00.0153.0148.0152.04.01.02.0
21112124140.00.0010.0000.0060.0000.00.078.00.427.07.066.0103.0169.06.00.0152.0147.0151.04.01.02.0
21122125142.00.0020.0020.0080.0000.00.074.00.436.05.042.0117.0159.02.01.0145.0143.0145.01.00.01.0